zhouxiangxin1998 commited on
Commit
8c31b30
β€’
1 Parent(s): 3e140f1
Files changed (1) hide show
  1. app.py +8 -8
app.py CHANGED
@@ -62,7 +62,7 @@ with demo:
62
  pd.read_csv('data/inverse_folding.csv'),
63
  height=99999,
64
  interactive=False,
65
- type=['markdown', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number'],
66
  )
67
  with gr.TabItem("πŸ† Structure Design Leaderboard", elem_id='structure-design-table', id=1,):
68
  with gr.Row():
@@ -70,7 +70,7 @@ with demo:
70
  pd.read_csv('data/structure_design.csv'),
71
  height=99999,
72
  interactive=False,
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- type=['markdown', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number'],
74
  )
75
  with gr.TabItem("πŸ† Sequence Design Leaderboard", elem_id='sequence-design-table', id=2,):
76
  with gr.Row():
@@ -78,7 +78,7 @@ with demo:
78
  pd.read_csv('data/sequence_design.csv'),
79
  height=99999,
80
  interactive=False,
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- type=['markdown', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number'],
82
  )
83
  with gr.TabItem("πŸ† Sequence-Structure Co-Design Leaderboard", elem_id='co-design-table', id=3,):
84
  with gr.Row():
@@ -86,7 +86,7 @@ with demo:
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  pd.read_csv('data/co_design.csv'),
87
  height=99999,
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  interactive=False,
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- type=['markdown', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number'],
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  )
91
  with gr.TabItem("πŸ† Motif Scaffolding Leaderboard", elem_id='motif-scaffolding-table', id=4,):
92
  with gr.Row():
@@ -94,7 +94,7 @@ with demo:
94
  pd.read_csv('data/motif_scaffolding.csv'),
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  height=99999,
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  interactive=False,
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- type=['markdown', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number'],
98
  )
99
  with gr.TabItem("πŸ† Antibody Design Leaderboard", elem_id='antibody-design-table', id=5,):
100
  with gr.Row():
@@ -109,7 +109,7 @@ with demo:
109
  pd.read_csv('data/protein_folding.csv'),
110
  height=99999,
111
  interactive=False,
112
- type=['markdown', 'number', 'number', 'number', 'number', 'number', 'number', 'number'],
113
  )
114
  with gr.TabItem("πŸ… Multi-State Prediction Leaderboard", elem_id='multi-state-prediction-table', id=7,):
115
  with gr.Row():
@@ -117,7 +117,7 @@ with demo:
117
  pd.read_csv('data/multi_state_prediction.csv'),
118
  height=99999,
119
  interactive=False,
120
- type=['markdown', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number'],
121
  )
122
  with gr.TabItem("πŸ… Conformation Prediction Leaderboard", elem_id='conformation-prediction-table', id=8,):
123
  with gr.Row():
@@ -125,7 +125,7 @@ with demo:
125
  pd.read_csv('data/conformation_prediction.csv'),
126
  height=99999,
127
  interactive=False,
128
- type=['markdown', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number'],
129
  )
130
 
131
 
 
62
  pd.read_csv('data/inverse_folding.csv'),
63
  height=99999,
64
  interactive=False,
65
+ datatype=['markdown', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number'],
66
  )
67
  with gr.TabItem("πŸ† Structure Design Leaderboard", elem_id='structure-design-table', id=1,):
68
  with gr.Row():
 
70
  pd.read_csv('data/structure_design.csv'),
71
  height=99999,
72
  interactive=False,
73
+ datatype=['markdown', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number'],
74
  )
75
  with gr.TabItem("πŸ† Sequence Design Leaderboard", elem_id='sequence-design-table', id=2,):
76
  with gr.Row():
 
78
  pd.read_csv('data/sequence_design.csv'),
79
  height=99999,
80
  interactive=False,
81
+ datatype=['markdown', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number'],
82
  )
83
  with gr.TabItem("πŸ† Sequence-Structure Co-Design Leaderboard", elem_id='co-design-table', id=3,):
84
  with gr.Row():
 
86
  pd.read_csv('data/co_design.csv'),
87
  height=99999,
88
  interactive=False,
89
+ datatype=['markdown', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number'],
90
  )
91
  with gr.TabItem("πŸ† Motif Scaffolding Leaderboard", elem_id='motif-scaffolding-table', id=4,):
92
  with gr.Row():
 
94
  pd.read_csv('data/motif_scaffolding.csv'),
95
  height=99999,
96
  interactive=False,
97
+ datatype=['markdown', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number'],
98
  )
99
  with gr.TabItem("πŸ† Antibody Design Leaderboard", elem_id='antibody-design-table', id=5,):
100
  with gr.Row():
 
109
  pd.read_csv('data/protein_folding.csv'),
110
  height=99999,
111
  interactive=False,
112
+ datatype=['markdown', 'number', 'number', 'number', 'number', 'number', 'number', 'number'],
113
  )
114
  with gr.TabItem("πŸ… Multi-State Prediction Leaderboard", elem_id='multi-state-prediction-table', id=7,):
115
  with gr.Row():
 
117
  pd.read_csv('data/multi_state_prediction.csv'),
118
  height=99999,
119
  interactive=False,
120
+ datatype=['markdown', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number'],
121
  )
122
  with gr.TabItem("πŸ… Conformation Prediction Leaderboard", elem_id='conformation-prediction-table', id=8,):
123
  with gr.Row():
 
125
  pd.read_csv('data/conformation_prediction.csv'),
126
  height=99999,
127
  interactive=False,
128
+ datatype=['markdown', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number'],
129
  )
130
 
131